Computational Methods for Detecting Multi-gene Relationships in Gene Expression Data

检测基因表达数据中多基因关系的计算方法

• 批准号: 121360-2012
• 负责人: Bonner, Anthony
• 金额: $ 1.24万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569182
• 关键词: Computational; Methods; Detecting; Multi; gene

Gene coexpression has emerged over the last several years as a powerful bioinformatics tool. Coexpressed genes may be involved in the same biological process, and thus coexpression networks are often used to investigate gene function. Methods for detecting, analyzing and clustering pairs of coexpressed genes are now well developed. Pairwise coexpression, however, is clearly too simplistic to describe the complex relationships between genes, since these relationships may involve multiple genes and can vary depending on the biological context. To address this limitation, we propose to develop computational methods for detecting and analyzing multi-gene relationships in gene expression data, and to use these methods to solve problems in molecular biology. These problems will include the reconstruction of genetic regulatory networks, the detection of modulators of gene regulation, and improved tools for gene function prediction. One of the main difficulties in detecting multi-gene relationships is the vast number of possible relationships which need to be considered. This is especially true in organisms with a large number of genes, such as plants and mammals. In such cases, separating the true relationships from the possible relationships poses challenging computational and statistical problems. To address these problems, we shall first develop methods of low computational complexity for detecting relatively simple multi-gene relationships. To avoid overfitting the data, we will develop methods for accurately estimating false positive rates. We will also develop heuristics for speeding up the computations while missing as few significant relationships as possible. To validate the methods, we will test them on both real and simulated gene expression data. Building upon these results, we will develop methods for detecting more-complex relationships involving larger numbers of genes.

基因共表达在过去几年中已经成为一个强大的生物信息学工具。 共表达的基因可能参与相同的生物学过程，因此共表达网络经常被用来研究基因功能。 用于检测、分析和聚类共表达基因对的方法现在已经发展得很好。 然而，成对共表达显然过于简单化，无法描述基因之间的复杂关系，因为这些关系可能涉及多个基因，并且可以根据生物学背景而变化。 为了解决这个问题，我们建议开发计算方法来检测和分析基因表达数据中的多基因关系，并使用这些方法来解决分子生物学中的问题。这些问题将包括重建基因调控网络，检测基因调控的调制器，以及改进的基因功能预测工具。 检测多基因关系的主要困难之一是需要考虑大量可能的关系。 对于具有大量基因的生物体（例如植物和哺乳动物）来说尤其如此。 在这种情况下，从可能的关系中分离出真正的关系会带来具有挑战性的计算和统计问题。为了解决这些问题，我们将首先开发低计算复杂度的方法来检测相对简单的多基因关系。为了避免过度拟合数据，我们将开发准确估计假阳性率的方法。 我们还将开发加速计算的算法，同时尽可能少地遗漏重要关系。为了验证这些方法，我们将在真实的和模拟的基因表达数据上对其进行测试。在这些结果的基础上，我们将开发检测涉及大量基因的更复杂关系的方法。